class_docstr: Draws randomly from the given dataset until adversarial examples for all
  inputs have been found.
  
  To pass data form the dataset to this attack, call :meth:`feed()`.
  :meth:`feed()` can be called several times and should only be called with
  batches that are small enough that they can be passed through the model.
  
  Args:
      distance : Distance measure for which minimal adversarial examples are searched.
class_docstr_tok: ["Draws","randomly","from","the","given","dataset","until","adversarial","examples","for","all","inputs","have","been","found",".","To","pass","data","form","the","dataset","to","this","attack",",","call",":","meth",":","`","feed","(",")","`",".",":","meth",":","`","feed","(",")","`","can","be","called","several","times","and","should","only","be","called","with","batches","that","are","small","enough","that","they","can","be","passed","through","the","model",".","Args",":","distance",":","Distance","measure","for","which","minimal","adversarial","examples","are","searched","."]
code: def feed(self, model: Model, inputs: Any) -> None:
      x = ep.astensor(inputs)
      del inputs
  
      self.raw_inputs.append(x)
      self.raw_outputs.append(model(x))
  
file_path: foolbox/foolbox/attacks/dataset_attack.py
imports: from typing import Union, Optional, Any, List
  import numpy
  import eagerpy
  from ..devutils import atleast_kd
  from ..models import Model
  from ..distances import Distance
  from ..criteria import Criterion
  from .base import FlexibleDistanceMinimizationAttack
  from .base import T
  from .base import get_criterion
  from .base import raise_if_kwargs
  from .base import verify_input_bounds
label_desc: Use image node_id 2 for calling the DatasetAttack obj's underlying member method code with example usage: obj.feed(model, inputs) without return types